Computer results for new nonlinear filtering algorithm

1982 ◽  
Vol 129 (2) ◽  
pp. 70 ◽  
Author(s):  
J.W. O'Loghlen ◽  
G.C. Wright
Keyword(s):  
Nonlinear Filtering ◽  
Filtering Algorithm

Development of Nonlinear Filtering Algorithms of Digital Half-Tone Images

2013 ◽  
pp. 278-304 ◽  
Author(s):  
E. P. Petrov ◽  
I. S. Trubin ◽  
E. V. Medvedeva ◽  
S. M. Smolskiy
Keyword(s):  
Discrete Time ◽  
Gaussian Noise ◽  
Nonlinear Filtering ◽  
Noise Immunity ◽  
Digital Communication ◽  
Additive White Gaussian Noise ◽  
Markovian Process ◽  
Radio Receiver ◽  
Filtering Algorithm ◽  
Communication Links

This chapter is devoted to solving the problem of algorithms and structures investigations for Radio Receiver Devices (RRD) with the aim of the nonlinear filtering of Digital Half-Tone Images (DHTI) representing the discrete-time and discrete-value random Markovian process with a number of states greater than two. At that, it is assumed that each value of the DHTI element is represented by the binary g-bit number, whose bits are transmitted via digital communication links in the presence of Additive White Gaussian Noise (AWGN). The authors present the qualitative analysis of the optimal DHTI filtering algorithm. The noise immunity of the optimal radio receiver device for the DHTI filtering with varying quantization and dimension levels is investigated.

A New Nonlinear Filter Method for Ballistic Target Tracking

2011 ◽  
Vol 135-136 ◽  
pp. 99-105
Author(s):  
Chun Ling Wu ◽  
Yong Feng Ju
Keyword(s):  
Kalman Filter ◽  
Target Tracking ◽  
Nonlinear Filtering ◽  
Quadrature Rule ◽  
Filter Method ◽  
Square Root ◽  
Numerical Accuracy ◽  
Filtering Algorithm ◽  
The Mean ◽  
Gauss Hermite Quadrature

In order to track the ballistic re-entry target, a new kind of ballistic target tracking algorithm, square-root quadrature Kalman filter (SRQKF) algorithm, was proposed. The proposed algorithm is the square-root implementation of the quadrature Kalman filter (QKF). The quadrature Kalman filter is a recursive, nonlinear filtering algorithm developed in the Kalman filtering framework and computes the mean and covariance of all conditional densities using the Gauss-Hermite quadrature rule. The square-root quadrature Kalman filter propagates the mean and the square root of the covariance. It guarantees the symmetry and positive semi-definiteness of the covariance matrix, improved numerical stability and the numerical accuracy, but at the expense of increased computational complexity slightly.

scholarly journals Maneuvering Target Tracking Algorithm Based on Interacting Multiple Models

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Gannan Yuan ◽  
Wei Zhu ◽  
Wei Wang ◽  
Bo Yin
Keyword(s):  
Target Tracking ◽  
Nonlinear Filtering ◽  
Information Matrix ◽  
Multiple Models ◽  
Quick Response ◽  
Multiple Model ◽  
Maneuvering Target Tracking ◽  
Filtering Algorithm ◽  
Maneuvering Target ◽  
Information Filter

Aiming at improving the accuracy and quick response of the filter in nonlinear maneuvering target tracking problems, the Interacting Multiple Models Cubature Information Filter (IMMCIF) is proposed. In IMMCIF, the Cubature Information Filter (CIF) is brought into Interacting Multiple Model (IMM), which can not only improve the accuracy but also enhance the quick response of the filter. CIF is a multisensor nonlinear filtering algorithm; it evaluates the information vector and information matrix rather than state vector and covariance, which can reduce the error of nonlinear filtering algorithm. IMM disposes all the models simultaneously through Markov Chain, which can enhance the quick response of the filter. Finally, the simulation results show that the proposed filter exhibits fast and smooth switching when disposing different maneuver models; it performs better than the IMMCKF and IMMUKF on tracking accuracy.

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